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Record W6990827098

Equity, Evidence & Ethics: Assessing the Implications of Outdoor Smoke-free Policies for Vulnerable Populations

2012· article· en· W6990827098 on OpenAlexfundno aff

Bibliographic record

VenueUKnowledge (University of Kentucky) · 2012
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsnot available
FundersCanadian Institutes of Health Research
KeywordsNucleofectionHyporeflexiaGestational periodTSG101Articular cartilage damagePopulation
DOInot available

Abstract

fetched live from OpenAlex

Smoke-free policies in outdoor settings are becoming increasingly common. Economic and social conditions tend to influence tobacco use patterns as well as exposure to secondhand smoke. Thus, active and passive smoking are both health equity and social justice concerns.\nSmoking on the Margins is a multi-component,mixed-methods project that applies an ethical framework to outdoor smoke-free policies in order to identify both concerns and opportunities to promote health and health equity.\nJustification for smoke-free policies\n Smoke-free spaces are primarily justified on the basis of three goals:\n1) Reducing exposure to secondhand smoke;\n2) Encouraging people to quit smoking; and\n3) Preventing youth smoking initiation.\nSmoke-free policies in parks and beaches may have a small positive population health impact. Such policies reduce secondhand smoke exposure by eliminating a combination of circumstances that create sufficient concentration of tobacco smoke to pose serious health risk; such bans may also facilitate smoking cessation or reduction for some people. There is little evidence to date,however, that smoke-free policies in parks and on beaches have an impact on the prevention of smoking initiation among youth.\nAs well, the documented positive benefits may be offset by other, unintended and/or inequitable burdens, such as when the stigmatization of smoking makes it harder for some smokers to quit or contributes to greater health inequalities.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score0.438

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.375
GPT teacher head0.456
Teacher spread0.081 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2012
Admission routes1
Has abstractyes

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